Animal Rescues Bivariate

Choropleth Facet Map

Photo by Sara Scarpa on Unsplash

Help! I’m stuck!

Well, it may be humorous to you, but it’s a very serious matter to the squirrels…
— Lisa Kleypas

Ingest

animal group and ward_code (to match with map geometry)

df <- read.csv("archetypes/animal-rescues/animal-rescues.csv", header = TRUE, encoding = "UTF-8")
df

Wrangle & Analyze

summarize by spatial unit (ward) and animal group

animal_by_boroughs <- df %>% 
  mutate(animal_group_parent = str_to_title(animal_group_parent)) %>% 
  count(borough_code, animal_group_parent, sort = TRUE) %>% 
  filter(animal_group_parent %in% c("Horse", "Cow", "Hamster", "Squirrel", "Lamb", "Sheep", "Snake", "Lizard", "Hedgehog", "Rabbit", "Ferret", "Fish", "Goat", "Pigeon", "Bull", "Tortoise")) %>% 
  ungroup() 

as.data.frame(unique(animal_by_boroughs$animal_group_parent))

animal_by_boroughs

Load maps

wards and regions

ltla_map <- st_read("archetypes/animal-rescues/local-authorities-lowertier-4-ltla-2019.geojson")

## Reading layer `LocalAuthorities-lowertier 4 LTLA-2019' from data source 
##   `C:\Users\jaybe\Desktop\kamino-dev\sources\10-maps\2-area\2-choropleth-facet\archetypes\animal-rescues\local-authorities-lowertier-4-ltla-2019.geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 382 features and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -7.557107 ymin: 49.76684 xmax: -7.556398 ymax: 49.76744
## Geodetic CRS:  WGS 84

ltla_map

Wrangle

enrich and filter map geometry, calculate centroid

# to focus on london boroughs
# london_bk <- filter(background_map, RegionNation == "London")
london <- filter(ltla_map, RegionNation == "London")

# enrich spatial data with quantitative data for choropleth
london_animals <- london %>%
  left_join(animal_by_boroughs, by = c("Lacode" = "borough_code")) %>% 
  # by filtering out, background will show
  filter(!is.na(animal_group_parent))
head(london_animals, n = 10)

# this is used to plot an aesthetic background
center <- st_coordinates(st_centroid(london$geometry))

Plot

color by bins, facet by animal group

theme_opts <- theme(
  text = element_text(family = "inconsolata", size = 16), 
  plot.title = element_text(color = "black", size = 16, face = "bold"),
  plot.subtitle = element_text(color = "black", size = 12),
  plot.caption = element_text(color = "#555555", size = 11),
  plot.background = element_blank(),
  axis.text = element_blank(),
  axis.line = element_blank(),
  axis.ticks = element_blank(),
  axis.title = element_blank(),
  panel.grid = element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank(),
  panel.background=element_rect(fill="white", colour="white"),
  panel.border = element_blank(),
  legend.position = "bottom",
  legend.title = element_blank(),
)

fill_pallete <- c("#3B454A", "#31739C", "#21B3CD", "#FFD349", "#E85E26")

v1 <- ggplot(london_animals) + 
  # bottom layer as a cirle background
  # geom_point(aes(x = center[1], y = center[2]), size = 150, shape = 21, fill = "#007EA7", stroke = 2) +
  # border outline
  geom_sf(data = london, aes(geometry = geometry), fill = "grey50", color = "white", size = 0.1) + 
  # the choropleth
  geom_sf(data = london_animals, aes(geometry = geometry, fill = n), color = "white", size = 0.1) +
  # color by bins from rescue count
  scale_fill_stepsn(colors = fill_pallete, n.breaks = 5) +
  # facet by animal group
  facet_wrap(~animal_group_parent, ncol = 4) + 
  labs(
    title = "Frequency of Rescues of Birds, Cats and Dogs in London from 2009-2021",
    subtitle = NULL,
    caption = NULL,
    x = NULL,
    y = NULL
  ) + 
  theme_minimal() + 
  theme_opts

girafe(ggobj = v1, width_svg = 16, height_svg = 13,
       options = list(opts_sizing(rescale = TRUE, width = 1.0))
)

References

citations for narrative and data sources

Narrative sources: This is a reproduction of an original idea from @jakekaupp, GO
Data sources: london.gov.uk, GO